Idiosyncratic Risk Premia and Momentum

Chichernea, Doina; Slezak, Steve L.

doi:10.1111/j.1475-6803.2013.12016.x

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…As robustness check, we also used DOLARVOLUME and TURNOVER as proxies for liquidity in all of our tests, and the results are qualitatively unchanged (untabulated). We also consider cumulative returns over the previous six months as a control for momentum (Chichernea andSlezak, 2013, andMcLean, 2010, show that IV and momentum may be related). In addition, Huang et al (2010) demonstrate that lagged return (as a control for reversals) is an important variable in cross-sectional return regressions.…”

Section: Control Variablesmentioning

confidence: 99%

Idiosyncratic Risk, Investor Base, and Returns

Chichernea¹,

Ferguson²,

Kassa

2015

Financial Management

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Section: Control Variablesmentioning

confidence: 99%

Idiosyncratic Risk, Investor Base, and Returns

Chichernea¹,

Ferguson²,

Kassa

2015

Financial Management

Self Cite

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“…We do not observe a significant correlation between return and lagged idiosyncratic risk in any market. This correlation analysis also reveals that our results are consistent with what has been reported in previous literature (Arena et al ., 2008; Ang et al ., 2009, Chichernea and Slezak, 2013).…”

Section: Methodology and Resultsmentioning

confidence: 99%

Size, conditional idiosyncratic risk and price momentum: an international study

Fan

Beyer

et al. 2023

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PurposeThis paper jointly examines how firm size and idiosyncratic risk impact momentum returns.Design/methodology/approachUsing regression analysis, the authors investigate how firm size and idiosyncratic risk impact price momentum. The authors review firm price data in 25 country markets in the Thomson Financial Datastream database from 1979 to 2009.FindingsThis study’s findings suggest price momentum is more significant among stocks with smaller size and higher idiosyncratic risk. The authors find that winner and loser portfolios have significantly smaller size and higher idiosyncratic risk than portfolios in the middle quintiles.Research limitations/implicationsThis study’s results are consistent with the notion that firm size matters in price momentum and mispricing is greatest for small firms because of the greater risk potential to arbitrageurs. In addition, this finding that firms with higher idiosyncratic risk have greater price momentum supports the idea that investors underreact to firm-specific information.Practical implicationsThis work finds evidence that investors underreact to firm-specific information. As such, these findings are of particular interest for investors looking to exploit opportunities for abnormal returns through price momentum trading.Originality/valueThis paper jointly examines the effects of firm size and idiosyncratic risk on momentum returns. This investigation considers these effects in the global markets. This work adds to the research base by illustrating that both winner and loser portfolios have significantly smaller size and higher idiosyncratic risk than portfolios in the middle quintiles. Also unique to this study, the authors capture the time-variation of expected IdioRisk and the asymmetric effects of volatility by using an exponential general autoregressive conditional heteroskedastic (EGARCH) model to calculate conditional idiosyncratic risk.

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“…Our paper relates to a string of studies in the momentum literature, which explore the incremental value of corporate fundamentals in the traditional price-momentum strategy. Prior studies have identified some firm fundamentals/characteristics that strengthen the price momentum, such as firm size and analyst coverage (Brennan, Jegadeesh, & Swaminathan, 1993;Chen & Zhao, 2012;Hong, Lim, & Stein, 2002;Zhang, 2006), earnings (Chan et al, 1996;Chordia & Shivakumar, 2006), book-to-market ratio (Asness et al, 2013;Daniel & Titman, 1999), turnover (Lee & Swaminathan, 2000), idiosyncratic return volatility (Chichernea & Slezak, 2013;Zhang, 2006), revenues, costs, and real options (Sagi & Seasholes, 2007), dividend payments (Asem, 2009), and, recently, seven major fundamentals to take advantage of big data (Huang, Zhang, Zhou, & Zhu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Investment momentum: A two‐dimensional behavioural strategy

Zhao

Zheng

2020

Int. J Fin Econ

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We propose an investment-momentum strategy of buying past winners with low investment and selling past losers with high investment, which simultaneously exploits two dimensions of market inefficiencies. The new strategy generates twice the monthly returns earned by either the price momentum or investment strategy (1.44% vs. 0.75% or 0.61%). Despite the diminishing anomalies in recent decades, the investment-momentum stays persistent. The mispricing-based strategy performs better in periods of high investor sentiment or for stocks with high limits-to-arbitrage, which is consistent with our expectations. Overall, we show that one can simultaneously use multiple dimensions of market inefficiency to attain superior performance.

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Idiosyncratic Risk Premia and Momentum

Cited by 6 publications

References 46 publications

Idiosyncratic Risk, Investor Base, and Returns

Idiosyncratic Risk, Investor Base, and Returns

Size, conditional idiosyncratic risk and price momentum: an international study

Investment momentum: A two‐dimensional behavioural strategy

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